Every Breath You Take: Probing the Properties of Particulate Pollution
Transcript of Every Breath You Take: Probing the Properties of Particulate Pollution
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Southern Ontario Centre for Atmospheric Aerosol Research UNIVERSITY of TORONTO
Every Breath You Take: Probing the Properties of Particulate
Pollution
Prof. Greg Evans
PHO Jan 2015
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Aerosol Particle Size Categories
Adapted from Brook R et.al Circulation 2005
nm mm μm
Ultrafine < 100 nm
Fine or PM2.5 < 2.5 μm
PM10 < 10 μm
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Particles: they are all different!
College Street June 2013
Fresh Black Carbon Coated Black Carbon
260 nm
1 µm
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• Used data from two clinical trials on atherosclerosis prevention
• Mapped study subjects to PM2.5 exposure
• Exposure was associated with atherosclerosis
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• Matching particulate concentrations and life expectancy data for two periods (1979-1983 and 1999-2000) in 51 metro areas
• Evaluated changes in life expectancy with changes in air pollution for the 2-decade period.
• A 10 µg m-3 particulate decrease in was associated with a 7.3 month increase in life expectancy.
The life expectancy change persistence even after controlled for socio-economic, demographic or smoking variables. 9
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Outline • Global scale: PM around the world • Regional scale: Sources and Impacts
• City Scale: Vehicle Emissions • Individual scale: Exposome, sensors
and toxicity
• I do not have any conflicts of interest to disclose related to this presentation
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SOCAAR: Research Overview Southern Ontario Centre for Atmospheric Aerosol Research
Anthropogenic Sources
Natural Sources
Aging: Size and Chemistry
Environmental Impact: Climate
Exposure
Health Impact
Field and lab research
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Acknowledgments Research Group
• Krystal Godri • Ezzat Jaroudi • Cheol Jeong • Rob Healy • Nathan Hilker • Maygan McGuire • Natalia Mykhaylova • Andrew Knox • Kelly Sabaliauskas • Jon Wang • Naomi Zimmerman
Funding and Partners • CFI/OIT/MRI • NSERC • CIHR • Environment Canada • Health Canada
All results should be considered as preliminary
Conclusions do not necessarily reflect views or position of funding agencies
I DO NOT have any conflicts of Interest to disclose related to this presentation
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Global Scale: Why Isn’t the Sky Blue?
Toronto Sao Paulo Photo by J Brook Photo carbonsolutions.com
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<30 µg/m3
Jan 12 2013
>800 µg/m3
Photo from Bill Bishop/Sinocism China Newsletter Photo from Flickr, bfishadow
Air quality in Beijing
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Global Scale: PM2.5 Exposure Satellite remote sensing (2001-06)
PM2.5
Env Res (120), 2013
Cardiopulmonary disease 8% (5.5-10.5) Lung Cancer 12.8% (5.9-18.5) Heart Disease 9.4% (6.6-11.8)
2 to 3 million deaths per year
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Global Particle Type Diversity
Global library of 25 particles types from 18 locations 21
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Diversity and Ubiquity Diversity: How externally mixed are the particles at a given site?
Ubiquity: How well represented is a given particle type across the sites?
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Monetizing Air Quality Impacts
• Where does the PM go?
Concentration (C)
Population (P)
• How many are exposed?
Health impact Financial cost
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Economic Burden from Ontario’s coal
based electricity generation
Particulate Matter Concentration Map
Source: Nanticoke
Damage = ∑C x P x SCR x E • C= PM2.5
• P = Population exposed
• SCR= Concentration – responce function
• E = Cost per unit damage
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•Health Cost • Estimated for Nanticoke
Station: • $5.7/MWh
Air Quality- Health Externalities from Electricity in Ontario
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Benefit of coal phase out: ~$300 million/year What if we reintroduce coal in 30 years? $450 to $900 million/year!
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Health co-benefit savings of energy conservation in homes
The health co-benefit of building a low energy home is ~7% of additional cost. Would be higher in higher population density region.
What if we built homes to a better building code to reduce electricity use?
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Identifying Sources: Receptor Modelling
Primary Emissions
Atmospheric Processing
Atmospheric Transport
Receptor Site
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TIME
Measurements at site
Correlate temporal trends of components
Identify factors, their contributions
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5
10
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Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12
PM
2.5
(µg/
m3 )
Mean ± SE
Pre-recession Coal phase out Post-recession
Application: Reduction of PM2.5 in Toronto
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Change in PM2.5 Source Contribution
2005 PM2.5 = 13 µg/m3
2010 PM2.5 = 10 µg/m3
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Do we know all the sources? Black Carbon Rich Source
• Similar amounts of black carbon in rural sites as in cities
• Suggests that it is not all due to traffic
Substantial source of black carbon found at
rural sites
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Population Living Near a Major Road
Location Distance from Road (m)
100 250 Toronto 1,240,000 (24%) 2,825,000 (56%)
Montreal 312,975 (9%) 888,160 (24%)
Vancouver 442,225 (21%) 1,030,320 (49%)
Ontario 2,370,785(19%) 5,622,845 (46%)
Canada 4,090,000 (13%) 10,260,000 (32%)
Evans et al Design of a Near-Road Monitoring Strategy for Canada
Report to Environment Canada 2011
Number of people (percent)
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10 million Canadians live within 250 m of a major road
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Density of Road Within
n HR 95% Upper
95% Lower
500 m 397 1.25 1.05 1.48 300 m 397 1.26 1.07 1.48 200 m 397 1.30 1.07 1.58
Morbidity of Lung Transplant Recipients and Proximity to Traffic
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Incidence of Bronchiolitis Obliterans Syndrome
Bhinder S. Chen H. et al J. Transplantation 2014
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Ultrafine particles on College Street
1. Evaluated Excess Air Pollution Temporal differences: rapid vs slow variations
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Ultrafine particles on College Street
1. Evaluated Excess Air Pollution Temporal differences: local vs regional signal
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Measurement of Emissions Factors
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Pollutant Unit EF BC g/kg 0.39 CO g/kg 9.5 NO g/kg 6.0 NOx g/kg 19 Particles 1015 #/kg 18
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Fleet Emission Factors: 155,000 vehicle plumes in four season
18%
plu
mes
had
un
dete
ctab
le N
Ox
72%
plu
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un
dete
ctab
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O
13% contributes 79% of total CO emissions
7% contributes 26% of total NOx emissions
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Particle Emission Factors: Fleet mean values
Black Carbon Particle Number
Highest 5% of vehicles emit: 64% of black Carbon and 37% of particles
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Drive by Testing
• New Gasoline Direct Injection (GDI) cars emit ~10 times more UFP
• Fuel additives vary seasonally and summer additives (e.g. toluene) increase UFP emissions
Can detect vehicle plume ~50% of the time
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Individual Level: Gene X Environment
Environmental Attributable Risk WHO, 2006 49 49
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Through the lens of individuals..
Human Biology
Research (populations to mechanisms)
Applications (public health)
Personalised Health
Individual
External Internal
Individualised Assessment
Populations
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UofT Sensor Array System
Circuit Board Manufacture Deployment
Includes sensors for: CO, NOx, VOC, O3 PM …..
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Training the array to measure CO concentrations
Sensor Array
CO Monitor
CO estimated based on array sensor, trained using data from this same day
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Environmental Exposure, Multiple
Pollutants
External Chemical
Environment
Internal Chemical Environment
Exposure
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Disease Internalised
Pollutant
Intermediate Phenotype/ Biomarkers
Chronic Disease
Pollutant Emission
Exposome
Time
Exposome: A way to link pollutants to chronic disease
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Summary • Particles are strongly linked to global disease yet
they come from many sources and are all different; we treat them as being the same
• Emissions are strongly linked to energy use. We need to monitor how changes in energy use alter air quality
• People are all different; we treat them as being the same. Coupling of gene and environment means we need to examine health impacts through the lens of individuals
• Measurements are essential to identifying their evolving impacts of health and climate
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Southern Ontario Centre for Atmospheric Aerosol Research UNIVERSITY of TORONTO
Thank You www.socaar.utoronto.ca
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Jan 23 2013 image courtesy of University of Utah/TimeScience.
January 2013: 75 µg/m3
Salt Lake City
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